Neoplastic transformation reprograms tumor and surrounding host cell metabolism, increasing nutrient consumption and depletion in the tumor microenvironment. Tumors uptake nutrients from neighboring normal tissues or the bloodstream to meet energy and anabolic demands. Tumor-induced chronic inflammation, a high-energy process, also consumes nutrients to sustain its dysfunctional activities. These tumor-related metabolic and physiological changes, including chronic inflammation, negatively impact systemic metabolism and physiology. Furthermore, the adverse effects of antitumor therapy and tumor obstruction impair the endocrine, neural, and gastrointestinal systems, thereby confounding the systemic status of patients. These alterations result in decreased appetite, impaired nutrient absorption, inflammation, and shift from anabolic to catabolic metabolism. Consequently, cancer patients often suffer from malnutrition, which worsens prognosis and increases susceptibility to secondary adverse events. This review explores how neoplastic transformation affects tumor and microenvironment metabolism and inflammation, leading to poor prognosis, and discusses potential strategies and clinical interventions to improve patient outcomes.

UNASSIGNED: The cachexia index (CXI), which consists of skeletal muscle, inflammation, and nutritional status, has been associated with prognosis in patients with hepatocellular carcinoma (HCC). We hypothesized that dynamic changes in CXI might be associated with long-term outcomes in HCC.
UNASSIGNED: This study comprised 131 patients who had undergone primary hepatic resection for HCC between 2008 and 2019. Preoperative CXI (pre-CXI) and postoperative CXI (post-CXI) were calculated by the following formula: skeletal muscle index x serum albumin level / neutrophil-to-lymphocyte ratio. Pre- and post-CXI were classified into two groups (high vs. low). We retrospectively investigated the association of perioperative dynamic changes in CXI with disease-free and overall survival.
UNASSIGNED: In multivariate analyses, negative HBs-antigen (p = 0.02), high serum PIVKA-II level (p < 0.01), poor tumor differentiation (p = 0.02), multiple tumors (p < 0.01), microvascular invasion (p < 0.01), partial resection (p < 0.01), postoperative complications (p < 0.01), and low-pre-CXI (p < 0.01) were significant predictors of disease-free survival, while high ICGR15 (p = 0.01), poor tumor differentiation (p < 0.01), multiple tumors (p = 0.01), postoperative complications (p < 0.01), low-pre-CXI (p < 0.01), and low-post-CXI (p < 0.01) were significant predictors of overall survival. Low-post-CXI was associated with older age (p = 0.045), larger tumor (p < 0.01), longer operation time (p = 0.047), greater intraoperative bleeding (p < 0.01), and intraoperative blood transfusion (p < 0.01). Moreover, dynamic changes in CXI were associated with overall survival in each subgroup of patients with low-pre-CXI (p = 0.02) or high-pre-CXI (p = 0.03).
UNASSIGNED: Not only post-CXI but also dynamic changes in CXI from pre- to post-hepatectomy can be a prognostic indicator of HCC, providing a compelling rationale for aggressive perioperative nutritional and physical interventions to improve long-term outcomes.

BACKGROUND: Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.
OBJECTIVE: This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia.
METHODS: We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process.
RESULTS: Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy.
CONCLUSIONS: We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

BACKGROUND: Cancer cachexia affects more than half of all cancer patients, reducing survival rates. Evidence-based approaches are urgently needed to optimize treatment.
METHODS: A systematic review and network meta-analysis were conducted to assess the effectiveness and safety of different pharmacotherapies for cancer cachexia. Three databases (PubMed, Cochrane Library, and Web of Science) were searched for the period from January 1, 2000, to March 20, 2024. The netmeta package in R software was used to calculate the pooled effect, employing a random effects model.
RESULTS: Seven placebo-controlled randomized trials involving 1421 patients were analyzed. Pairwise analysis showed that body weight increases were 4.6 kg (95% confidence interval [CI] 0.83-8.37 kg) for olanzapine, 3.82 kg (95% CI 0.73-6.91 kg) for espindolol (20 mg), 2.36 kg (95% CI 1.84-2.89 kg) for anamorelin (100 mg), and 1.31 kg (95% CI 0.42-2.19 kg) for anamorelin (50 mg). In terms of safety profiles, olanzapine demonstrated the lowest odds ratio when compared to placebo, at 0.26 (95% CI 0.07-0.94), followed by anamorelin (50 mg) at 0.86 (95% CI 0.30-2.48), and anamorelin (100 mg) at 0.89 (95% CI 0.42-1.88). However, network meta-analysis could not confirm the superiority of olanzapine over anamorelin in terms of efficacy and safety.
CONCLUSIONS: Both olanzapine and anamorelin are useful in improving body weight in patients with cancer cachexia. Personalization may be helpful for different patients.

TGFβ-activated kinase-1 (TAK1) is phosphorylated during both muscle growth and muscle wasting. To understand how this can lead to such opposite effects, we first performed multiplex kinase array of mouse embryonic stem cells with and without stimulation of TAK1 to determine its potential downstream targets. The phosphorylation of these targets was then compared in three different models: hypertrophic longissimus muscle of Texel Sheep, tibialis anterior muscle of mice with cancer-induced cachexia and C2C12-derived myofibers, with and without blockade of TAK1 phosphorylation. In both Texel sheep and in cancer-induced cachexia, phosphorylation of both TAK1 and p38 was increased. Whereas p90RSK was increased in Texel sheep but not cachexia and the phosphorylation of HSP27 and total Jnk were increased in cachexia but not Texel. To understand this further, we examined the expression of these proteins in C2C12 cells as they differentiated into myotubes, with and without blockade of TAK1 phosphorylation. In C2C12 cells, decreased phosphorylation of TAK1 leads to reduced phosphorylation of p38, JNK, and HSP27 after 16 hours and muscle fiber hypertrophy after three days. However, continuous blockade of this pathway leads to muscle fiber failure, suggesting that the timing of TAK1 activation controls the expression of context-dependent targets.

Cancer cachexia is a complex systemic wasting syndrome. Nutritional mechanisms that span energy intake, nutrient metabolism, body composition, and energy balance may be impacted by, and may contribute to, the development of cachexia. To date, clinical management of cachexia remains elusive. Leaning on discoveries and novel methodologies from other fields of research may bolster new breakthroughs that improve nutritional management and clinical outcomes. Characteristics that compare and contrast cachexia and obesity may reveal opportunities for cachexia research to adopt methodology from the well-established field of obesity research. This review outlines the known nutritional mechanisms and gaps in the knowledge surrounding cancer cachexia. In parallel, we present how obesity may be a different side of the same coin and how obesity research has tackled similar research questions. We present insights into how cachexia research may utilize nutritional methodology to expand our understanding of cachexia to improve definitions and clinical care in future directions for the field.

(1) Background: Impaired nutritional status in systemic sclerosis (SSc) is prevalent. (2) Objective: This study aimed to identify pre-cachexia and malnutrition in SSc patients and to estimate the effectiveness of a high-protein oral nutritional supplement (ONS) in improving their nutritional status. (3) Materials and methods: The SSc population comprised 56 patients and a control group of 49 healthy persons. After a baseline clinical evaluation, bioelectrical impedance analysis (BIA), and laboratory tests, SSc patients were divided into well-nourished, pre-cachectic, and malnourished categories. SSc patients with a nutritional disbalance received a high-protein ONS once daily for 3 months. Patients were reassessed at 3 and 12 months after inclusion in the study. (4) Results: SSc patients, in comparison to the control group, had a significantly lower seven-point SGA value [6(0) vs. 7(1), p < 0.001)], lean tissue mass [LTM, 35.1 (10.5) vs. 40.1 (10.10), p = 0.008], and lean tissue index [LTI, 13.5 (3) vs. 14.9 (4), p = 0.009]. Of the 56 SSc patients, 40 (71.4%) were well nourished, 5 (8.9%) had pre-cachexia, and 11 (19.7%) were malnourished. A high-protein ONS in the pre-cachexia group stabilized the SGA value, anthropometric measurements, and BIA after 3 and 12 months. In malnourished patients, it significantly improved the SGA value [5(0) vs. 6(0), p = 0.002], LTI [12.1 (2.1) vs. 12.7 (3.2), p = 0.021] and LTM [31.1 (7.7) vs. 35.1 (9.1), p = 0.021], and that effect remained stable at 12 months. (5) Conclusion: Malnutrition is a common complication of SSc that can be improved with nutritional intervention.

UNASSIGNED: Cancer cachexia is a common debilitating weight loss syndrome in advanced cancer, particularly lung cancer. Omega-3 fatty acids, eicosapentaenoic acid and docosahexaenoic acid, with their immune-modulating effects, have been used to improve the nutritional status of patients with cancer cachexia.
UNASSIGNED: Evaluate the effects of omega-3 fatty acids in change in weight and lean body/skeletal mass, and health-related quality of life scores (HRQoL) in patients with advanced non-small cell lung cancer and cancer cachexia.
UNASSIGNED: Clinical trials from electronic databases and unpublished literature (date of last search 20 December 2023) were independently reviewed and evaluated by authors for their methodological quality. Data from eligible trials were extracted and analyzed in a meta-analysis.
UNASSIGNED: Six trials were included. Five trials (354 patients) assessed change in weight; 2 trials (132 patients) assessed change in lean body/skeletal mass and HRQoL scores (Global Health and Physical Functioning subscales). There is a significant difference in change in weight (mean difference [MD]: 1.22, 95% CI: 1.05-1.38, P < .01) and HRQoL scores (Global Health [MD: 14.40, 95% CI: 9.22-19.59, P < .01] and Physical Functioning [MD: 10.38, 95% CI: 8.50-12.27, P < .01] subscales) favoring the omega-3 fatty acids group. The change in lean body/skeletal mass is not significant (MD: 2.05, 95% CI: -0.55 to 4.66, P = .12).
UNASSIGNED: Among patients with advanced non-small cell lung cancer and cancer cachexia, supplementation with omega-3 fatty acids leads to a significant increase in weight and HRQoL scores but not in change in lean body/skeletal mass.

Drugs have actions that may be classified as therapeutic effects and side effects; side effects are actions that do not contribute to therapeutic benefit. Some side effects are neutral; others, experienced as undesirable or unpleasant, are recorded as adverse effects. Some drug actions are therapeutic for some disorders and adverse for others; or therapeutic during acute illness and adverse during maintenance treatment. As an example, anticholinergic action may be adverse when a tricyclic antidepressant is used to treat depression but therapeutic when the drug is used to treat irritable bowel syndrome with diarrhea. In clinical practice, side or adverse effects of a drug may be leveraged to manage troublesome symptoms. As an example, the sedative effect of a low dose of trazodone may be useful for some patients with insomnia. With this background, studies have examined whether the increase in appetite and weight associated with olanzapine and mirtazapine may be effective against anorexia and cachexia associated with cancer and cancer chemotherapy. The subject is important because cachexia may be present in 30%-50% of patients with cancer (with higher prevalence in patients with more advanced cancer) and because the presence of cachexia is associated with a higher risk of disease progression and mortality. Many randomized controlled trials (RCTs) have examined pharmacologic interventions such as progestins, corticosteroids, anamorelin, and medical cannabis for cancer related cachexia; most results have been disappointing. A recent RCT found that olanzapine (2.5 mg/d for 12 weeks) improved appetite, weight, other nutritional parameters, and quality of life in patients with locally advanced or metastatic cancer treated with chemotherapy. Another RCT, however, found that mirtazapine (30 mg/d for 8 weeks) brought no nutritional or anthropometric gain in patients with cancer and anorexia. It is concluded that olanzapine but not mirtazapine merits further investigation in patients with cancer who have anorexia and cachexia.

BACKGROUND: Cancer-associated cachexia (CAC) is a metabolic syndrome contributing to therapy resistance and mortality in lung cancer patients (LCP). CAC is typically defined using clinical non-imaging criteria. Given the metabolic underpinnings of CAC and the ability of [18F]fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography (PET)/computer tomography (CT) to provide quantitative information on glucose turnover, we evaluate the usefulness of whole-body (WB) PET/CT imaging, as part of the standard diagnostic workup of LCP, to provide additional information on the onset or presence of CAC.
METHODS: This multi-centre study included 345 LCP who underwent WB [18F]FDG-PET/CT imaging for initial clinical staging. A weight loss grading system (WLGS) adjusted to body mass index was used to classify LCP into \'No CAC\' (WLGS-0/1 at baseline prior treatment and at first follow-up: N = 158, 51F/107M), \'Dev CAC\' (WLGS-0/1 at baseline and WLGS-3/4 at follow-up: N = 90, 34F/56M), and \'CAC\' (WLGS-3/4 at baseline: N = 97, 31F/66M). For each CAC category, mean standardized uptake values (SUV) normalized to aorta uptake () and CT-defined volumes were extracted for abdominal and visceral organs, muscles, and adipose-tissue using automated image segmentation of baseline [18F]FDG-PET/CT images. Imaging and non-imaging parameters from laboratory tests were compared statistically. A machine-learning (ML) model was then trained to classify LCP as \'No CAC\', \'Dev CAC\', and \'CAC\' based on their imaging parameters. SHapley Additive exPlanations (SHAP) analysis was employed to identify the key factors contributing to CAC development for each patient.
RESULTS: The three CAC categories displayed multi-organ differences in . In all target organs, was higher in the \'CAC\' cohort compared with \'No CAC\' (P < 0.01), except for liver and kidneys, where in \'CAC\' was reduced by 5%. The \'Dev CAC\' cohort displayed a small but significant increase in of pancreas (+4%), skeletal-muscle (+7%), subcutaneous adipose-tissue (+11%), and visceral adipose-tissue (+15%). In \'CAC\' patients, a strong negative Spearman correlation (ρ = -0.8) was identified between and volumes of adipose-tissue. The machine-learning model identified \'CAC\' at baseline with 81% of accuracy, highlighting of spleen, pancreas, liver, and adipose-tissue as most relevant features. The model performance was suboptimal (54%) when classifying \'Dev CAC\' versus \'No CAC\'.
CONCLUSIONS: WB [18F]FDG-PET/CT imaging reveals groupwise differences in the multi-organ metabolism of LCP with and without CAC, thus highlighting systemic metabolic aberrations symptomatic of cachectic patients. Based on a retrospective cohort, our ML model identified patients with CAC with good accuracy. However, its performance in patients developing CAC was suboptimal. A prospective, multi-centre study has been initiated to address the limitations of the present retrospective analysis.